On 22nd October 2026, the Department of Computer Science and Engineering at New Horizon College of Engineering organized an insightful hands-on workshop titled “Build an End-to-End Enterprise-Grade RAG System” for its 3rd-semester students.
The session was led by industry experts Mr. Anand Jha, Senior Solution Architect at Kipi.ai, and Ms. Akanksha Jagtap, Senior AI Engineer and Business Consultant at AWA Solutions. Bringing their extensive professional experience in technology and artificial intelligence, the resource persons introduced students to the core concepts and practical applications of Retrieval-Augmented Generation (RAG) systems.
The workshop began with an introduction to the importance of RAG in today’s rapidly evolving AI landscape. The speakers explained how Large Language Models (LLMs) can be combined with external knowledge sources to overcome standard model limitations and generate accurate, relevant, and context-aware responses. They highlighted the critical role of information retrieval in enhancing generative AI capabilities and its growing necessity in building modern intelligent applications.
During the technical segments, the experts provided an overview of RAG system architecture and workflow. They detailed how information from external documents and knowledge bases is retrieved and seamlessly integrated with language models to produce meaningful outputs. Students gained step-by-step insights into key architectural components, including information retrieval, knowledge integration, and response generation techniques.
Through live demonstrations and hands-on activities, the resource persons guided students through the practical development of an end-to-end RAG system. The workshop also emphasized real-world enterprise applications, covering crucial considerations such as scalability, reliability, and deployment architecture to help organizations make effective use of their internal knowledge bases.
Throughout the interactive event, the resource persons emphasized the importance of continuous skill development in artificial intelligence. They encouraged students to explore emerging AI tools, experiment with practical projects, and strengthen their understanding of LLMs and retrieval mechanisms to stay industry-ready.
The session concluded with dynamic discussions on future possibilities of RAG systems in enterprise environments, leaving students inspired to apply advanced generative AI techniques in their upcoming academic and professional endeavors.